Alcohol & Alcoholism Vol. 43, No. 2, pp. 192–197, 2008 Advance Access publication 25 November 2007

doi:10.1093/alcalc/agm156

ALCOHOL CONSUMPTION, %CDT, GGT AND BLOOD PRESSURE CHANGE DURING ALCOHOL TREATMENT A. M. BAROS*, T. M. WRIGHT, P. K. LATHAM, P. M. MILLER and R. F. ANTON Charleston Alcohol Research Center, MUSC, Charleston SC 29425, USA (Received 16 April 2007; first review notified 18 June 2007; in revised form 6 August 2007; accepted 11 September 2007; advance access publication 25 November 2007) Abstract — Aims: Blood pressure (BP) changes in alcohol-dependent individuals during a 12-week alcohol relapse prevention study were examined in light of drinking status and biomarkers of alcohol consumption [carbohydrate-deficient transferrin (%CDT) and gamma-glutamyl transpeptidase (GGT)]. Methods: Of 160 randomized alcoholic individuals, 120 who had hypertension and in whom daily drinking data was available, at 6 and 12 weeks of treatment were included. The impact of alcohol consumption on change in systolic BP (SBP) and diastolic BP (DBP) was examined. Further analysis determined the relationship between BP and alcohol-use biomarkers. Results: A significant effect of complete abstinence on both SBP (−10 mmHg; P = 0.003) and DBP (−7 mmHg; P = 0.001) when compared to any drinking (SBP and DBP = −1 mmHg) was observed. At week 12, participants with a positive %CDT (
2.6) had 7 mmHg greater SBP (P = 0.01) and DBP (P < 0.001) than those with negative %CDT. Participants with positive GGT (
50 IU) had 10 mmHg greater SBP (P = 0.12) and 9 mmHg greater DBP (P = 0.03) than those with negative GGT. The percent change in SBP was correlated with percent change in %CDT (P = 0.003) but not GGT (P = ns). The percent change in DBP was correlated with both percent change in %CDT (P < 0.0001) and GGT (P = 0.03). Conclusions: Abstinence from alcohol significantly decreased the BP and a positive relationship between BP and both alcohol-use biomarkers was illustrated. Since %CDT is more specific than GGT for heavy alcohol consumption, clinicians may monitor the role of alcohol in hypertension using %CDT as a supplemental aid, providing an objective assessment of drinking to influence BP treatment decisions.

INTRODUCTION High blood pressure (BP) is an important cause of morbidity and mortality. It is the leading cause of stroke and heart failure, as well as the second most common precursor to end-stage renal disease (US Department of Health and Human Services, 2003). Identified risk factors for high BP include advanced age, obesity, excess sodium intake, physical inactivity and, important to this article, excessive alcohol consumption (Whelton et al ., 2002). The relationship between BP and alcohol consumption is somewhat complex. Light to moderate alcohol consumption has been associated with reduced risk for coronary heart disease (CHD) and subsequent reduction in total mortality (Shaper et al ., 1994; Di Castelnuovo et al ., 2002; Mukamal et al ., 2003). However, heavy alcohol consumption (
four standard drinks/day for females and
five standard drinks/day for males) is clearly associated with increased BP. The association between ‘heavy’ alcohol intake and increased BP in males and females appears to be independent of other factors, including obesity and smoking (Campbell et al ., 1999). Recommendations for the treatment of high BP include decreased alcohol consumption, if not complete cessation. It is advised that hypertensive individuals limit their alcohol intake to no more than two-drinks a day for men and one-drink a day for women (US Department of Health and Human Services, 2003). Frequently, clinicians do not identify alcohol as being etiologic in high BP, leading to the prescription of antihypertensive medications without addressing alcohol consumption. Possible explanations include clinician’s concern about offending patients by asking about alcohol consumption *Author to whom correspondence should be addressed at: Medical University of South Carolina, 67 President Street, PO Box 250861, Charleston, SC 29425, USA; E-mail: [email protected]

(Kaariainen et al ., 2001), practitioner time constraints, and patients’ hesitancy to provide accurate drinking information. Nevertheless, alcohol consumption assessment can be crucial for successful treatment. Even with pharmacological treatment, only 50% of hypertensive patients are able to achieve and maintain their BPs at or below health-targeted limits (Hajjar and Kotchen, 2003). Heavy alcohol consumption may be an important cause of treatment-resistant hypertension, potentially by interfering with the pharmacological action of antihypertensive medications and with adherence to physician recommendations designed to treat high BP (Miller et al ., 2005). A common concern in clinical practice is the inconsistency between a patient’s self-reported alcohol consumption and actual alcohol consumption. Under these conditions, laboratory markers of alcohol consumption can play an important role in identifying alcohol-induced clinical conditions including hypertension. Gamma-glutamyl transpeptidase (GGT), %carbohydrate-deficient transferrin (%CDT), and their combination are increasingly being used to detect heavy drinking and provide objective markers of risk for alcohol-related diseases (Fleming and Mundt, 2004; Miller, 2004; Miller et al ., 2006). Serum GGT levels of
50 IU are associated with significantly higher BPs as well as with a diagnosis of hypertension (Sillanaukee et al ., 1998). Sillanaukee and colleagues have demonstrated a significant association between BP and a mathematical combination of GGT and %CDT (Sillanaukee et al ., 2001). The relationship between %CDT and BP requires further clarification, as other authors have reported a positive relationship between elevated GGT level and CHD, but an inverse relationship between elevated CDT level and CHD (Jousilahti et al ., 2002). As %CDT is a specific marker of heavy alcohol consumption, higher %CDT values alone may also be associated with higher BP and a reduction of %CDT during attempts at alcohol reduction might predict lower BP. Many individuals entering alcohol treatment

 The Author 2007. Published by Oxford University Press on behalf of the Medical Council on Alcohol. All rights reserved

ALCOHOL, BIOMARKERS, AND BLOOD PRESSURE

centers have elevated BP and it is unclear if this increase in BP is reflective of acute alcohol withdrawal or chronic high alcohol use. Therefore, evaluating BP in newly abstinent alcoholics over time using state-of-the-art drinking assessment could address this issue as well. This report evaluates individuals participating in a clinical alcohol relapse prevention trial independent of their BP. The aims of the study were to (i) evaluate changes in BP over time in relation to changes in drinking status and (ii) examine the relationship between biological markers of alcohol consumption and BP changes over time.

MATERIALS AND METHODS Recruitment and baseline assessment Subjects included alcohol-dependent participants in a randomized clinical trial that investigated naltrexone combined with either cognitive behavioural or motivational enhancement therapy for alcohol dependence (Anton et al ., 2005). Participants were seeking outpatient treatment for alcoholism. The participant recruitment, inclusion/exclusion criteria, and baseline assessments are described in detail elsewhere (Anton et al ., 2005). Briefly, all subjects were dependent on alcohol but not on other substances (except nicotine), did not have other major psychiatric diagnoses, and were medically stable. Liver enzymes (ALT, AST) of all participants had to be less than 2.5 times the upper limit of normal at the time of randomization. Study procedures Randomized subjects (N = 160) were evaluated for alcohol and substance abuse by a technician at baseline and at the end of weeks 2, 6, and 12. BP (while seated), blood for liver function tests (ALT and AST) and biomarkers of alcohol consumption (%CDT and GGT), and measurements of alcohol consumption (see below) were obtained at each of these visits. BP was taken via the same electronic BP monitoring device (DINAMAP ProCare 120) that was calibrated every 6 months for accuracy. Measurement of alcohol consumption Daily alcohol consumption was measured by a calendarbased self-report procedure, the time line follow back method, reported to be accurate over a number of months retrospectively (Sobell et al ., 1988; Sobell and Sobell, 2000). In this method, each individual indicated how much he/she drank on each day during a specified period of time (7–90 days) on a calendar uniquely structured for that study visit. Mnemonics such as special dates and events are put on the calendar to assist in this process. Also, in our hands, a set of standardsized glasses are provided into which the subject pours water from a picture to indicate their usual drink size. The volume is subsequently measured and converted into standard drinks (12 oz beer (4–5% alcohol), 5-oz wine, or 1.5-oz spirits (40% alcohol). This method was used to quantify daily alcohol consumption for the 30 days prior to study participation and during the study as indicated above. This method

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has been utilized in the most advanced treatment trials e.g. the COMBINE Study (Anton et al ., 2006). Biological assays %CDT was measured via micro-column separation and Elisa assay method (Anton et al ., 2001) using test kits purchased from Biorad Inc. (Hercules, CA) at the Clinical Neurobiology Labs at the Medical University of South Carolina (Raymond F. Anton, M.D., Director). Inter- and intra-assay coefficients of variation are below 10%. GGT was measured via Abbott (Santa Clara, CA) Aeroset auto-analyser using standard reagents and controls with inter- and intra-coefficient of variations below 5%. Statistics Data were derived from 120 of the 160 participants in the alcohol treatment clinical trial (Anton et al ., 2005), irrespective of treatment group assignment, and for whom a BP reading at the 12-week research period was available. All analyses were conducted using the Statistical Package for Social Sciences (SPSS 11.5 analytic package). Baseline variables (age, gender, ethnicity, marital status, alcohol dependence scale score, drinks per drinking day past 90-days, %days of heavy drinking past 90-days, standard drink units past 90-days, BMI, and nicotine use) were examined for predictive validity of pre-study SBP and DBP by linear regression analysis. During the trial the impact of drinking status on BP at week 12 and the relationship of biomarkers (%CDT and GGT) to BP were analysed by ANCOVA (using study entry BP as a covariate). Non-parametric analysis (Spearman’s Rho) was used to determine the relationship between percent (week 12 baseline ×100%) change in BP, GGT, and %CDT over the course of 12 weeks.

RESULTS Demographic and baseline characteristics of the 120 participants providing data on BPs at week 12 were similar to those of the intent to treat analysis participants (N = 160) (Anton et al ., 2005) (Table 1). Subjects were mostly Caucasian, male, Table 1. Demographics of study sample (N = 120) Age (years) Gender (% male) Ethnicity (% Caucasian) % Married Alcohol dependence scale Drinksa per drinking day past 90-days % Days heavy drinkingb past 90-days Standard drink units past 90-days Body Mass Index Nicotine use

44 ± 9 77.5% 86.7% 57% 15 ± 7 12 ± 6 74 ± 23 873 ± 549 26 ± 4 53%

a A drink is defined as 12 oz of beer, 1.5 oz of liquor or 5 oz of wine. b A heavy drinking day is
five drinks for men or
four drinks for women.

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A. M. BAROS et al . Table 2. Impact of drinking status on blood pressure during trial at week 12 (end of treatment) Any drinking (N = 74)

Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) a

Total abstinence (N = 46)

Baseline

Week 12

Change

Baseline

Week 12

Change

136 ± 17 82 ± 10

135 ± 18 81 ± 11

−1 ± 13 −1 ± 10

139 ± 20 83 ± 11

129 ± 15 76 ± 12

−10 ± 18a − 7 ± 10b

P = 0.003 compared to any drinking.

b

P = 0.001 compared to any drinking.

and married, drinking 12 ± 6 drinks per drinking day over the 90-days prior to randomization with an average of 74% of those drinking days being heavy drinking days. Linear regression analysis indicated that gender (P = 0.02) was a predictor of DBP at baseline while increasing age (P = 0.03) was associated with higher baseline SBP. BMI was significantly correlated with SBP (r = 0.28, P = 0.002) and DBP (r = 0.24, P = 0.009). Ethnicity, martial status, alcohol dependence scale score, drinks per drinking day past 90-days, %days heavy drinking past 90-days, standard drink units past 90-days, and nicotine use were not significantly related to BP. The impact of total abstinence versus any drinking on BP during the 12 weeks of treatment is shown in Table 2. Total abstinence during the 12-week treatment period reduced SBP by 10 mmHg (P = 0.003) and DBP by 7 mmHg (P = 0.001). In comparison, those who reported any drinking had only a 1 mmHg reduction in SBP and DBP, respectively. The levels of both SBP and DBP, and those of biological markers (%CDT and GGT) at weeks 0, 6, and 12 are shown in Table 3. BP and both biological markers of alcohol consumption, demonstrated a significant reduction over time (P < 0.001). Reductions in %CDT and GGT were the most pronounced between week 0 and week 6 (P < 0.001), the period that saw the greatest reduction in both SBP and DBP. A significant overall increase in %CDT was observed between week 6 and week 12 (P < 0.001), which is consistent with previous data indicating the timeframe of increasing drinking for those who relapse (Anton et al ., 1996; Myrick et al ., 2001; Anton et al ., 2002). To address the issue of relapse, participants with normal or high biological variables at week 12 were examined separately. Table 4 shows the relationship of BP to %CDT and GGT status at week 12. Participants with negative (normal) %CDT ( week 12 P = 0.003. c P < 0.001 post hoc week 0 > week 6 P = 0.004; week 0 > week 12 P < 0.0001. d P < 0.001 post hoc week 0 > week 6 P < 0.0001; week 0 > week 12 P = 0.001; week 6 < week 12 P < 0.0001. e P < 0.0001 post hoc week 0 > week 6 P < 0.0001; week 0 > week 12 P < 0.0001. a

that those with a greater reduction in %CDT had a greater reduction in both SBP and DBP, while those with a greater elevation in %CDT had a corresponding greater increase in SBP and DBP. A significant association between the magnitude in GGT change and both SBP (P > 0.05, Rho = 0.09) and DBP (P = 0.03, Rho = 0.20) change was observed, but this was only significant for DBP. Figure 1 panels C and D show that those who had a greater reduction in GGT had a greater reduction in SBP and DBP, while those who had a greater increase in GGT had a greater elevation of SBP and DBP. During the baseline assessments and at the end of study interview (week 12) 17 of the 120 participants reported being prescribed and ingesting medications used to treat hypertension (anti-hypertensives). The demographics of the 17 participants prescribed and ingesting antihypertensive medications during the trial were not different than that of the other 103 participants. Of the 17 participants who had prescribed BP medication, 7 reported total abstinence and 10 reported drinking during the 12-week treatment period. The general direction of a reduction in both SBP (−9 mmHg in those who were abstinent and −4 mmHg in those who were drinking) and DBP (−4 mmHg in those who were abstinent and −1 mmHg in those who were drinking) was seen. However, this reduction from baseline was not significant, secondary to the small sample size. Although the 17 participants taking prescribed BP medications had higher, but not significantly higher, SBP and DBP throughout the trial, the relationship between BP and biomarkers was the same as the rest of the participants.

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Table 4. Blood pressure in relation to positive or negativea %carbohydrate-deficient transferrin (%CDT) and gamma-glutamyl transpeptidase (GGT) at week 12 (end of treatment)

Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg)

Positive %CDT

Negative %CDT

Positive GGT

Negative GGT

N = 51

N = 66

N = 30

N = 87

137 ± 18b 83 ± 12c

130 ± 15b 76 ± 11c

140 ± 18 85 ± 12d

130 ± 16 76 ± 11d

Positive CDT = (
2.6%), positive GGT (
50 IU). Negative refers to values within the nondrinking range of normal. b P = 0.01. c P < 0.001. d P = 0.03. a

A 140

B 140

100

100

60

60

20

20

-20

-20

-60

-60 Rho = 0.2 P = 0.003

-100 -140 -40

-20

0

20

Rho = 0.34 P